Energy benchmarking and ventilation related energy saving potentials for small and medium sized enterprises in Greater Toronto area
Bibliographic record
Abstract
In the past several years, energy benchmarking has become a very popular tool for the estimation of energy consumption and overall performance of buildings. More recently, industrial energy benchmarking has attracted attention all over the world, due to ever-increasing energy demands. Industrial facility ventilation is one of the most overlooked components in terms of overall industrial sector energy consumption. Therefore, a proper assessment and manage of energy can lead to a great reduction of energy usage, as shown in different small and medium industrial plant case studies. Although several articles and reports that have previously discussed ventilation analysis of industrial facilities in Ontario, energy benchmarking has never been conducted on ventilation. Therefore, the purpose of this thesis is to present a detailed energy benchmarking method and analyzing energy consumption and savings based on ventilation energy consumption. An energy benchmarking analysis was conducted in different small to medium sized facilities in the Greater Toronto Area (GTA), based on ventilation analysis. It was determined from the analysis that the typical and inefficient performing facilities can reduce average of 9% of their total natural gas consumption from total ventilation, 25% from transmission heat loss and 10% from infiltration loss compared to the top performing facility among all the audited facilities in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".